Compound object separation

ABSTRACT

Representations of an object can comprise two or more separate sub-objects, producing a compound object. Compound objects can affect the quality of object visualization and threat identification. As provided herein, a compound object can be separated into sub-objects based on object morphological properties (e.g., an object&#39;s shape, surface area). Further, a potential compound object can be split into sub-objects, for example, eroding one or more outer layers of volume space (e.g., voxels) from the potential compound object. Additionally, a volume of a representation of the sub-objects in an image can be reconstructed, for example, by generating sub-objects that have a combined volume approximate to that of the compound object. Furthermore, sub-objects, which can be parts of a same physical object, but may have been erroneously split, can be identified and merged using connectivity and compactness based techniques.

BACKGROUND

Security at airports and in other travel related areas is an importantissue given today's sociopolitical climate, as well as otherconsiderations. One technique used to promote travel safety is throughbaggage inspection. Often, an imaging apparatus is utilized tofacilitate baggage screening. For example, an x-ray machine may be usedto provide security personnel with a substantially two dimensional viewof the contents of a bag and/or a computed axial tomography (CAT) devicemay be used to provide two and/or three dimensional views of objects.After viewing images provided by the imaging apparatus, securitypersonnel may make a decision as to whether the baggage is safe to passthrough the security check-point or if further (hands-on) inspection iswarranted.

Current screening techniques and systems can utilize automated objectrecognition in images from an imaging apparatus, for example, whenscreening for potential threat objects inside luggage. These systems canextract an object from an image, and compute properties of theseextracted objects. Properties of scanned objects can be used fordiscriminating an object by comparing with known properties of threatitems. It can be appreciated that an ability to discriminate potentialthreats may be reduced if an extracted object comprises multipledistinct physical objects. Such an extracted object is referred to as acompound object.

A compound object can be made up of two or more distinct items. Forexample, if two items of similar density are lying side by side and/ortouching each other, a security scanner system may extract the two itemsas one single compound object. Because the compound object actuallycomprises two separate objects, however, properties of the compoundobject may not be able to be effectively compared with those of knownthreat and/or non-threat items. As such, for example, luggage containinga compound object may unnecessarily be flagged for additional (hands-on)inspection because the properties of the compound object resembleproperties of a known threat object. This can, among other things,reduce the throughput at a security checkpoint. Alternatively, acompound object that should be inspected further may not be soidentified because properties of a potential threat object in thecompound object are “contaminated” or combined with properties of one ormore other (non-threat) objects in the compound object, and these“contaminated” properties (of the compound object) more closely resemblethose of a non-threat object than those of a threat object.

Compound object splitting can be applied to objects in an attempt toimprove threat item detection, and thereby increase the throughput andeffectiveness at a security check-point. Compound object splittingessentially identifies potential compound objects and splits them intosub-objects. Compound object splitting involving components withdifferent densities may be performed using a histogram-based compoundobject splitting algorithm. Other techniques include using surfacevolume erosion to split objects. However, erosion when used as astand-alone technique to split compound objects can lead to undesirableeffects. For example, erosion can reduce a mass of an object, andindiscriminately split objects that are not compound, and/or fail tosplit some compound objects. Additionally, in these techniques, erosionand splitting may be applied universally, without regard to whether anobject is a potential compound object at all.

SUMMARY

Aspects of the present application address the above matters, andothers. According to one aspect, a system for splitting compound objectsinto one or more sub-objects in a subject image resulting fromsubjecting one or more objects to imaging using an imaging apparatusincludes an entry control configured to identify a potential compoundobject based on an object's features, a compound object splitterconfigured to generate sub-objects from the potential compound object byeroding one or more layer of volume space from the potential compoundobject, and a volume reconstructor configured to generate image datacomprising distinct sub-objects having a combined volume approximate toa volume of the potential compound object.

According to one aspect, a method for separating a compound object intosub-objects in an image generated by an imaging apparatus comprisesidentifying a potential compound object based on an object's features,splitting a potential compound object into sub-objects comprisingeroding one or more outer layers of volume space from the potentialcompound object, and reconstructing a volume of image data for thesub-objects comprising generating sub-objects having a combined volumeapproximate of the compound object.

According to one aspect, a system of separating a compound object intosub-objects in an image generated by a computer tomography (CT) scannercomprises an entry control configured to identify a potential compoundobject based on an object's features, in image data comprising one ormore objects scanned by a CT scanner, a volume reconstructor configuredto generate image data comprising distinct sub-objects having a combinedvolume approximate to a volume of the potential compound object, aconnectivity merger configured to merge distinct sub-objects in theimage data having a connectivity ratio within a pre-determinedconnectivity threshold, and a compactness merger configured to mergedistinct sub-objects in the image data that have a compactness ratiowithin a pre-determined compactness threshold.

According to one aspect, a computer usable medium comprising computerreadable programming configured to separate a compound object intosub-objects in an image generated by an imaging apparatus, which whenexecuted on a computing device, causes the computing device to identifya potential compound object based on an object's features, split apotential compound object into sub-objects comprising eroding one ormore outer layers of volume space from the potential compound object,reconstruct a volume of image data for the sub-objects comprisinggenerating sub-objects having a combined volume approximate to a volumeof the compound object, merge connected sub-objects, split from apotential compact object, that have a connectivity ratio meeting apre-determined connectivity threshold, and merge compact sub-objects,split from a potential compact object, that have a compactness ratiomeeting a pre-determined compactness threshold.

Those of ordinary skill in the art will appreciate still other aspectsof the present invention upon reading and understanding the appendeddescription.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram illustrating an environment whereincompound object splitting of objects in an image may be implemented, asprovided herein.

FIG. 2 is a component block diagram illustrating one or more componentsof an environment wherein compound object splitting of objects in animage may be implemented as provided herein.

FIG. 3 is a component block diagram illustrating details of one or morecomponents of an environment wherein compound object splitting ofobjects in an image may be implemented as provided herein.

FIG. 4 is another component block diagram illustrating details of one ormore components of an environment wherein compound object splitting ofobjects in an image may be implemented as provided herein.

FIG. 5 is a schematic block diagram illustrating one embodiment of oneor more components of an environment wherein compound object splittingof objects in an image may be implemented as provided herein.

FIG. 6 is a schematic block diagram illustrating one embodiment of oneor more components of an environment wherein compound object splittingof objects in an image may be implemented as provided herein.

FIG. 7 is a flow chart diagram of an example method for compound objectsplitting in an image produced by imaging of one or more objects.

FIG. 8 is a flow chart diagram of one embodiment of an example portionof a method for object splitting in an image produced by imaging of oneor more objects.

FIG. 9 is an illustration of example objects subjected to a portion of amethod for object splitting in an image produced by imaging of one ormore objects.

FIG. 10 is a flow chart diagram of one embodiment of an example portionof a method for object splitting in an image produced by imaging of oneor more objects.

FIG. 11 is an illustration of example objects subjected to a portion ofa method for object splitting in an image produced by imaging of one ormore objects.

FIG. 12 is a flow chart diagram of one embodiment of an example portionof a method for object splitting in an image produced by imaging of oneor more objects.

FIG. 13 is a flow chart diagram of one embodiment of an example portionof a method for object splitting in an image produced by imaging of oneor more objects.

FIG. 14 is an illustration of an example computer-readable mediumcomprising processor-executable instructions configured to embody one ormore of the provisions set forth herein.

DETAILED DESCRIPTION

The claimed subject matter is now described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the claimed subject matter. It may beevident, however, that the claimed subject matter may be practicedwithout these specific details. In other instances, structures anddevices are illustrated in block diagram form in order to facilitatedescribing the claimed subject matter.

Systems and techniques for separating a compound object representationinto sub-objects in an image generated by subjecting one or more objectsto imaging using an imaging apparatus (e.g., a computed axial tomography(CAT) image of a piece of luggage under inspection at a security stationat an airport) are provided herein. That is, in one embodiment,techniques and systems for splitting compound objects of similardensity, having a relatively weak connection, into distinct sub-objects.

FIG. 1 is an illustration of an example environment 100 in which asystem may be employed for identifying potential threat containingobjects, from a class of objects, inside a container that has beensubjected to imaging using an imaging apparatus (e.g., a CAT scanner).In the example environment 100 the imaging apparatus comprises an objectscanning apparatus 102, such as a security scanning apparatus (e.g.,used to scan luggage at an airport). The scanning apparatus 102 may beused to scan one or more objects 110 (e.g., a series of suitcases at theairport). The scanning apparatus typically comprises a radiation source104 (e.g., an X-ray tube), an array of radiation detectors 106 (e.g.,X-ray detectors), a rotator 112 (e.g., a gantry motor) for rotating theradiation source 104 and detectors 106 around the object(s) beingscanned 110, and a conveyor 108 configured to convey the object(s) 110from an upstream portion of the object scanning apparatus 102 to adownstream portion. It will be appreciated that, while the exampleenvironment utilizes an x-ray scanning apparatus, the systems andtechniques, described herein, are not limited to x-rays or scanningdevices. For example, the system may utilize an infrared imaging deviceto generate images based on infrared imaging of one or more objects.

As an example, a computer tomography (CT) security scanner 102 thatincludes an X-ray source 104, such as an X-ray tube, can generate a fan,cone, wedge, or other shaped beam of X-ray radiation that traverses oneor more objects 110, such as suitcases, in an examination region. Inthis example, the X-rays are emitted by the source 104, traverse theexamination region that contains the object(s) 110 to be scanned, andare detected by an X-ray detector 106 across from the X-ray source 104.Further, a rotator 112, such as a gantry motor drive attached to thescanner, can be used to rotate the X-ray source 104 and detector 106around the object(s) 110, for example. In this way, X-ray projectionsfrom a variety of perspectives of the suitcase can be collected, forexample, creating a set of X-ray projections for the object(s). Whileillustrated as a CT system, those of ordinary skill in the art willunderstand that other implementations such as line scanners are alsocontemplated. As yet another example, the radiation source 104 anddetector 106 may remain stationary while the object is rotated.

In the example environment 100, a data measurement system 114 isoperably coupled to the scanning apparatus 102, and is typicallyconfigured to collect information and data from the detector 106, andmay be used to compile the collected data into projection space data 150for an object 110. As an example, X-ray projections may be acquired ateach of a plurality of angular positions with respect to the object 110.Further, as the conveyor 108 conveys the object(s) 110 from an upstreamportion of the object scanning apparatus 102 to a downstream portion(e.g., conveying objects parallel to the rotational axis of the scanningarray), the plurality of angular position X-ray projections may beacquired at a plurality of points along an axis of the conveyor withrespect to the object(s) 110. In one embodiment, the plurality ofangular positions may comprise an X and Y axis with respect to theobject(s) being scanned, while the conveyor axis may comprise a Z axiswith respect to the object(s) being scanned.

In the example environment 100, an image extractor 116 is coupled to thedata measurement system 114, and is configured to receive the data 150from the data measurement system 114 and generate image data 152indicative of the scanned object 110 using a suitable analytical,iterative, and/or other reconstruction technique (e.g., backprojectingfrom projection space to image space).

In one embodiment, the image data 152 for a suitcase, for example, mayultimately be displayed on a monitor 134 (e.g., desktop or laptopcomputer) for human observation. In this embodiment, an operator mayisolate and manipulate the image, for example, rotating and viewing thesuitcase from a variety of angles, zoom levels, and positions.

It will be appreciated that, while the example environment 100 utilizesthe image extractor 116 to extract image data from the data 150generated by the data measurement system 114, for example, for asuitcase being scanned, the techniques and systems, described herein,are not limited to this embodiment. In another embodiment, for example,image data may be generated by an imaging apparatus that is not coupledto the system. In this example, the image data may be stored onto anelectronic storage device (e.g., a CD ROM, hard-drive, flash memory) anddelivered to the system electronically.

In the example environment 100, in one embodiment, an object and featureextractor 118 may receive the data 150 from the data measurement system114, for example, in order to extract objects and features 154 from thescanned items(s) 110 (e.g., a carry-on luggage containing items). Itwill be appreciated that the systems, described herein, are not limitedto having an object and feature extractor 118 at a location in theexample environment 100. For example, the object and feature extractormay be a component of the image extractor 116, whereby image data 152and object features 154 are both sent from the image extractor 116. Inanother example, the object and feature extractor 118 may be disposedafter the image extractor 116 and may extract object features 154 fromimage data 152. Those skilled in the art may devise alternativearrangements for supplying image data 152 and object features 154 to theexample system.

In the example environment 100, an entry control 120 may receive imagedata 152 and object features 154 for the one or more scanned objects110. The entry control 120 can be configured to identify a potentialcompound object in the image data 152 based on an object's features. Inone embodiment, the entry control 120 can be utilized to select objectsthat may be compound objects 156 for processing by a system for compoundobject splitting. In this embodiment, for example, object features 154(e.g., properties of an object in an image, such as an Eigen-box fillratio) can be computed prior to the entry control 120 and compared withpre-determined features for compound objects (e.g., features extractedfrom known compound objects during training of a system) to determinewhether the one or more objects are compound objects. In this example,objects that are not determined to be potential compound objects by theentry control 120 may not be sent through the compound object splittingsystem.

In the example environment 100, a compound object splitter 122 receivesimage data comprising a potential compound object 156 from the entrycontrol 120. The compound object splitter 122 can be configured togenerate sub-objects from the potential compound object by eroding oneor more layers of volume space from the potential compound object (e.g.,layers of voxels from a representation of the potential compound objectin a three-dimensional image).

As an example, one or more surface layer of voxels can be removed from arepresentation of the potential compound object in an image, which mayeffectively split the potential compound object into two or moresub-objects 158. However, in this example, if the potential compoundobject remains compound after removal of a first layer of voxels thecompound object splitter 122 can continue to remove layers until thepotential compound object is separated into two or more sub-objects 158.Alternatively, more than one layer of voxels can be consecutivelyremoved before checking if the potential compound object is separatedinto two or more sub-objects 158.

In the example environment 100, a volume reconstructor 124 can beconfigured to generate image data 160 comprising distinct sub-objectshaving a combined volume approximate a volume of the potential compoundobject. In one embodiment, for example, those voxels that were erodedfrom the potential compound object by the compound object splitter 122can be returned to the sub-objects 158. In this example, respectivelayers of eroded voxels can be returned to an adjacent sub-object (e.g.,the voxel was adjacent to that portion of the potential compound objectwhen it was eroded), thereby increasing a volume of the sub-objects.

Further, in this embodiment, a number of sub-objects can be maintainedwhile the respective eroded voxels are returned to the sub-objects, forexample, by layer. In this way, for example, the volume reconstructedsub-objects 160 can have a volume approximate to the volume of thepotential compound object from which they came, while maintaining anumber of sub-objects separated by the compound object splitter 122.

In the example environment 100, in one embodiment, image data for volumereconstructed sub-objects 160 can be sent to a merging component 128,which comprises a connectivity merger 130. In this embodiment, theconnectivity merger 130 can be configured to merge distinct sub-objectshaving a connectivity ratio within a pre-determined connectivitythreshold, for example, generating image data comprising mergedsub-objects 162. While the compound object splitter 122 can separate apotential compound object into sub-objects, for example, there may becircumstances where a sub-object is not a separate object but a physicalpart of a distinct physical object (e.g., an undesirable splitting ofthe potential compound object has occurred).

When such undesirable splitting occurs, in one embodiment, theconnectivity merger 130 can compare connection information betweensub-objects with a threshold ratio, for example, to determine whetherthe sub-objects should be merged.

In the example environment 100, in one embodiment, a merging componentmay comprise a compactness merger 132, which can be configured to mergedistinct sub-objects that have a compactness ratio within apre-determined compactness threshold, for example, generating image datathat comprises merged sub-objects 162. As an example, a physical objectthat is not a compound object (e.g., not made up of more than oneobject) is typically considered to be compact (e.g., when placed in abox, they fill a large portion of the box). However, in this example,objects that are randomly placed in a bag for scanning, resulting in animage comprising a representation of a compact object (e.g., made up ofmore than one separate physical object), are typically disposed in amanner that is not compact (e.g., when placed in a box, they fill asmaller portion of the box).

In one embodiment, the compactness merger 132 can compare a compactnessratio of two or more sub-objects to a threshold, to determine whetherthe sub-objects should be merged. In this way, for example,representations of physical objects that have been undesirably separatedin the image data for the physical objects can be merged back togetheras a single physical object for potential threat detection.

It will be appreciated that the systems and techniques, describedherein, are not limited to having the connectivity merger 130 andcompactness merger 132 disposed as described. Those skilled in the artmay devise alternate arrangements and embodiments for these components.For example, the connectivity merger 120 may receive volumereconstructed sub-object image data 160 and determine whether anysub-objects can be merged, then pass the resulting data to thecompactness merger 130 to determine whether any remaining sub-objectscan be merged. Further, in another example, respective merger componentsmay act independently upon volume reconstructed sub-object image data160, yielding a combination of merged and non-merged sub-object imagedata 162.

In the example environment 100, a threat determiner 126 can receiveimage data for an object, which can comprise volume reconstructedsub-objects 160, and/or a combination of merged sub-objects andnon-merged sub-objects 162. The threat determiner 126 can be configuredto compare the image data to one or more pre-determined thresholds,corresponding to one or more potential threat objects. It will beappreciated that the systems and techniques provided herein are notlimited to utilizing a threat determiner, and may be utilized forseparating compound objects without a threat determiner.

Information concerning whether a scanned object is potentially threatcontaining and/or information concerning sub-objects 164 can be sent toa terminal 134 in the example environment 100, for example, comprising adisplay that can be viewed by security personal at a luggage screeningcheckpoint. In this way, in this example, real-time information can beretrieved for objects subjected to scanning by a security scanner 102.

In one aspect, separate physical objects that comprise similar densitiesand lie in such proximity to each other that, when subjected to imaging,can yield an image that represents the separate objects as a singleobject. Such representations are commonly referred to as “compoundobjects,” and systems described herein can separate these compoundobjects into sub-objects, that may comprise the separate physicalobjects. However, one may wish to select merely compound objects tosubject to separation, for example, to increase computational efficiencyand reduce a possibility of separation error. In one embodiment, thesystems described herein can separate an object at “weak connectivitypoints;” however, areas of weak connectivity can be present in objectsthat are not compound. Therefore, an entry control may be used toidentify a potential compound object in image data.

FIG. 2 is a component block diagram illustrating one embodiment 200 ofan entry control 120, which can be configured to identify a potentialcompound object based on an object's features. The entry control 120 cancomprise a feature threshold comparison component 202, which can beconfigured to compare the respective one or more feature values 154 to acorresponding feature threshold 250.

In one embodiment, image data 152 for an object in question can be sentto the entry control 120, along with one or more corresponding featurevalues 154. In this embodiment, feature values 154 can include, but notbe limited to, an object's shape properties, such as an Eigen-box fillratio (EBFR) for the object in question. As an example, objects having alarge EBFR typically comprise a more uniform shape; while objects havinga small EBFR typically demonstrate irregularities in shape. In thisembodiment, the feature threshold comparison component 202 can compareone or more object feature values with a threshold value for that objectfeature, to determine which of the one or more features indicate acompound object for the object in question.

In the example embodiment 200, the entry control 120 can comprise anentry decision component 204, which can be configured to identify apotential compound object based on a combination of results from thefeature threshold comparison component 202. In one embodiment, thedecision component 204 may identify a potential compound object based ona desired number of positive results for respective object features, thepositive results comprising an indication of a potential compoundobject. As an example, in this embodiment, a desired number of positiveresults may be one hundred percent, which means that if one of theobject features indicates a non-compound object, the object may not besent to be separated 162. However, in this example, if the object inquestion has the desired number of positive results (e.g., all of them)then the image data for the potential compound object can be sent forseparation 156.

In another aspect, separating a potential compound object intosub-object can be performed by erosion of volume space around theobject. For example, one or more layers of volume space can be erodedfrom the object until the object is separated at a weak connection point(e.g., a point where two sub-objects may be thinly connected). Once thepotential compound object is separated into two or more sub-objects, forexample, one may wish to individually identify and count the newlycreated sub-objects.

FIG. 3 is a component block diagram of one example embodiment 300 of acompound object splitter 122, which can be configured to generatesub-objects. The example embodiment of the compound object splitter 122comprises an eroded voxel identifier 302 configured to identify a set oferoded voxels 350 as an outer layer of the potential compound object. Inthis embodiment, data for one or more respective sets of eroded voxelscan be stored, for example, for later use. For example, a representationof a potential compound object in an image can be comprised of voxels,and the eroded voxel identifier 302 can identify one or more outer layerof voxels of the representation in the image to be eroded.

In the example embodiment 300, the compound object splitter 122 furthercomprises a volume space eroder 304, which can be configured to erodeone or more surface layer of voxels from a potential compound object. Asan example, the volume space eroder 304 can remove the outer layer ofvoxels from the potential compound object that was identified by theeroded voxel identifier 302. An eroded layer counter 306 can beconfigured to count a number of layers eroded from the compact object,for example, after respective layers are removed by the volume spaceeroder 304.

The compound object splitter 122 further comprises a sub-objectidentifier 308, which can be configured to identify sub-objects from apotential compound object subjected to volume space erosion. As anexample, the sub-object identifier 308 can be used to determine whetherthe compound object has been separated into two or more sub-objects(e.g., using connected component analysis) after respective volume spaceerosions (e.g., outer layers of voxels removed). In this example, if thesub-object identifier 308 determines that the potential compound objecthas not been separated, the image data can be sent back to the erodedvoxel identified 302 for erosion of a next outer layer of voxels.

Further, in this example embodiment 300, the sub-object identifier 308can comprise a sub-object labeler 310, which may be configured togenerate an image of differently labeled sub-objects from the potentialcompound object. For example, if the sub-object identifier 308determines that the potential compound object has been separated intosub-objects, the sub-object labeler 310 can generate different labelsfor the respective sub-objects in an image comprising representations ofthe sub-objects. In this way, in this example, respective sub-objectsmay be identified as separate and different objects in the image.

Additionally, in this example embodiment 300, the sub-object identifier308 can comprise a sub-object counter 312, which can be configured tocount a number of sub-objects from the potential compound object. Forexample, a correct number of sub-objects can be associated with thepotential compound object that was separated in order to facilitatelater volume reconstruction and possible merging.

In another aspect, sub-object data 158 generated by the compound objectsplitter 122 can comprise merely representations of sub-objects having avolume that has been reduced by the erosion of volume space from thesurface. In one embodiment, one may wish to maintain an original volumeof respective sub-objects, for example, so that a threat determiner 126may be able to generate accurate results for the sub-objects. In thisexample, if the sub-objects have less volume, they may not match up withthreshold comparisons utilized by the threat determiner 126.

FIG. 4 is a component block diagram of an example embodiment 400 of avolume reconstructor 124 that can be configured to generate image datacomprising distinct sub-objects having a combined volume approximate avolume of the potential compound object. In this example embodiment 400,the volume reconstructor 124 comprises a neighboring eroded voxelidentifier 402, which can be configured to identify one or more voxelsin a set of eroded voxels that are spatially connected to one or morevoxels in a sub-object.

For example, the neighboring eroded voxel identifier 402 can identifythose voxels from one or more set of voxels (e.g., comprising surfacelayers of voxels from the potential compound object) that were eroded bythe compound object splitter 122 (e.g., as identified by the erodedvoxel identifier, see FIG. 3, 302) that were spatially connected tovoxels in the one or more sub-objects. In this example, a neighborhoodof respective voxels is checked to determine if the voxel neighbors anysub-object. A first set of voxels to be identified may be a last layerof voxels that were eroded, as they are likely to be spatially connectedto the outer layer of voxels in the sub-objects. In this way, in thisexample, an appropriate sub-object for those voxels that were eroded canbe identified, corresponding to a sub-object to which they werespatially connected before erosion.

In the example embodiment 400, the volume reconstructor 124 furthercomprises an eroded voxel dissolver 404, which can be configured todissolve a set of eroded voxels into the sub-objects. As an example,those eroded voxels identified by the neighboring eroded voxelidentifier 402 as being spatially connected to voxels in the sub-objectcan be returned to the sub-object by the eroded voxel dissolver 404. Inthis way, for example, one or more sets of previously eroded voxels(e.g., comprising one or more respective layers of eroded voxels) can bedissolved into (e.g., returned to) the sub-objects to which they werepreviously spatially connected prior to erosion. In this example, anumber of sub-objects can be maintained while a volume for respectivesub-objects is increased.

In the example embodiment 400, the volume reconstructor 124 furthercomprises a neighboring dilated voxel identifier 406, which can beconfigured to identify one or more voxels in a set of dilated voxels 450that are spatially connected to one or more voxels in a sub-object. Inone embodiment, dilated voxels 450 may be generated by a voxel dilatorcomponent of a bulk object identification system, for example, in whichthe compound object separation system, described herein, may bedisposed. In this embodiment, dilated voxels can comprise a subset of aplurality of image elements that comprise an object in an imaging image.

For example, during object identification and separation in the bulkobject identification system, a representation of the object in questionmay have been dilated to incorporate one or more outer layers of voxelsthat met some threshold for dilation. In this example, dilated voxels450 that neighbor voxels in the sub-objects, and an appropriate locationfor the dilated voxels, can be identified. In this way, a location inthe image of the sub-objects for one or more sets of dilated voxels canbe determined, for example, in sequence by layer.

In the example embodiment 400, the volume reconstructor 124 furthercomprises a dilated voxel dissolver 408 configured to dissolve a set ofdilated voxels into the sub-objects. For example, those dilated voxels450 identified by the neighboring dilated voxel identifier 406 can bedissolved into the sub-objects identified by the neighboring dilatedvoxel identifier 406. In this way, in this example, image data 160 canbe generated that comprises identified sub-objects having a volumeapproximate to a volume of the potential compound object from which theywere separated.

In another aspect, unwanted splitting of compound objects can occur whena representation of a non-compound physical object in an image isseparated into two or more sub-objects. This undesirable separation canoccur, for example, with multi-part objects (e.g., explosives), porousobjects, certain electronics, and where an imaging artifact may dividean object. Certain properties of sub-objects can be analyzed todetermine whether undesirable separation may have occurred.

In one embodiment, connectivity of two or more sub-objects may beanalyzed to determine whether undesirable separation occurred. FIG. 5 isa component block diagram of an example embodiment 500 of a connectivitymerger 130 that can be configured to merge distinct sub-objects having aconnectivity ratio within a pre-determined connectivity threshold. Inthis embodiment 500, image data for volume reconstructed sub-objects canbe sent to a connectivity determiner 502, which can be configured todetermine a connectivity ratio of distinct sub-objects 550. For example,when the sub-objects were separated by a connected object splitter (seeFIG. 3, 122) and volume reconstructed by a volume reconstructor (seeFIG. 4, 124), an outer layer of volume space units (e.g., voxels) fromrespective sub-objects can be spatially connected (e.g., a portion ofthe sub-objects can be touching each other). In this example, theconnectivity determiner 502 can calculate a ratio 550 of a number ofconnected surface voxels between respective sub-objects to the volume ofthe sub-object.

In the example embodiment 500, the connectivity merger 130 can furthercomprise a connectivity threshold comparison component 504, which can beconfigured to compare the connectivity ratio of the distinct sub-objects550 to a pre-determined connectivity threshold 552. For example, athreshold ratio 552 for connection between sub-objects can be determinedby testing a variety of objects by subjecting them to similar scanningand compound object splitting. In another example, a connectivity ratiothreshold 552 may be calculated based on certain variables associatedwith the system. It will be appreciated that the systems and techniques,described herein, are not limited to determining a connectivity ratiothreshold 552 in any particular manner. Those skilled in the art maydevise alternate methods for determining a connectivity ratio threshold552.

In this embodiment, the threshold comparison component 504 can compare aconnectivity ratio of distinct sub-objects 550 to a connectivity ratiothreshold 552 to determine whether the sub-objects should be merged intoone physical object, or remain as separated sub-objects. For example, ifa connectivity ratio of distinct sub-objects 550 is greater than theconnectivity ratio threshold 552, the connectivity merger 130 cangenerate image data comprising a representation of merged sub-objects162 (e.g., the sub-objects can be merged back into a distinct physicalobject). Otherwise, in this example, the connectivity merger 130 cangenerate image data still comprising a representation of distinctsub-objects 162.

Further, in another example, where a potential compound object may havebeen separated into more than two sub-objects, the connectivity merger130 can determine connectivity and compare to a threshold for respectivesub-objects. In this way, in this example, resulting image data 162 maycomprise sub-objects which are not distinct physical objects and shouldnot have been split by a splitter but were not merged by theconnectivity merger. As another example, resulting image data 162 maycomprise a combination of merged sub-objects and unmerged sub-objects,such as where a potential compound object was split into threesub-objects, but should have been split into two.

In this aspect, in another embodiment, compactness of two or moresub-objects may be analyzed to determine whether undesirable separationoccurred. As an example, while components of a same object are expectedto form spatially compact combinations, parts of an actual compoundobject may be expected to be arranged in random positions andorientations with respect to each other, thereby forming less compactcombinations. In this example, a property that can describe objectcompactness is an Eigen-box fill ratio (EBFR).

FIG. 6 is a component block diagram of an example embodiment 600 of acompactness merger 132 that can be configured to merge distinctsub-objects that have a compactness ratio within a pre-determinedcompactness threshold. In this example embodiment 600, image data 162(e.g., from the connectivity merger) comprising representations ofsub-objects is sent to an Eigen-box generator 602, which can beconfigured to generate an Eigen-box 650 for the respective distinctsub-objects and a union of the sub-objects, for example, that wereseparated from a potential compound object. As an example, an Eigen-box650 can be generated that accommodates an entirety of an object whilehaving a least amount of volume (e.g., a smallest box that can fit theobject), for respective sub-objects, and for the union of thesub-objects. In this example, Eigen-boxes can be generated for unions ofpairs of sub-objects that are connected, which may result in severalcombinations of pairs of sub-objects from a same potential compoundobject (e.g., if a potential compound object was separated into morethan two sub-objects).

In the example embodiment 600, the compactness merger 132 furthercomprises a fill ratio determiner 604 that can be configured tocalculate a fill ratio 652 for an object in an Eigen-box (e.g., anEBFR). As an example, for respective sub-objects and for the unions ofthe sub-objects, the fill ratio determiner 604 can calculate how muchthe object fills the Eigen-box.

In the example embodiment 600, the compactness merger 132 furthercomprises a compactness ratio generator 606, which can be configured tocompare a combination of EBFRs of the respective sub-objects to an EBFRof a union of the respective sub-objects, for example, to generate acompactness ratio for the sub-objects 654. As an example, thecompactness ratio generator 606 may determine an increase in an EBFRfrom respective sub-objects to the EBFR of the union of the sub-objects.In this example, the increase may be reflected as a compactness ratiofor the pair of sub-objects 654. Further, in this example, compactnessratios may be determined for respective pairs of sub-objects (e.g.,where a potential compound object was split into more than twosub-objects), whereby the EBFR for the respective sub-objects of therespective pairs can be compared to the union of the sub-object for therespective pairs.

In the example embodiment 600, the compactness merger 132 furthercomprises a compactness threshold comparison component 608, which can beconfigured to compare the compactness ratio of the sub-objects 654 to apre-determined compactness threshold 656. For example, a compactnessthreshold 656 for compactness of sub-objects can be determined bytesting a variety of objects by subjecting them to similar scanning andcompound object splitting. In another example, a compactness threshold656 may be calculated based on certain variables associated with thesystem. It will be appreciated that the systems and techniques,described herein, are not limited to determining a compactness threshold656 in any particular manner. Those skilled in the art may devisealternate methods for determining a compactness threshold 656.

In this embodiment, the compactness threshold comparison component 608can compare a compactness ratio of the sub-objects 654 to a compactnessthreshold 656 to determine whether the sub-objects should be merged intoone physical object, or remain as separated sub-objects. For example, ifa compactness ratio of the sub-objects 654 is greater than thecompactness threshold 656, the compactness merger 132 can generate imagedata comprising a representation of merged sub-objects 164 (e.g., forthe pair of sub-objects tested for compactness). Otherwise, in thisexample, the compactness merger 132 can generate image data stillcomprising a representation of distinct sub-objects 164.

It will be appreciated that, while the embodiments described above showthe compactness merger 132 disposed after the connectivity merger 130,the techniques and systems described herein are not limited to theseembodiments. Those skilled in the art may devise alternate arrangementsfor these components in a system. For example, both the connectivitymerger 130 and compactness merger 132 may receive image data 160,comprising volume reconstructed sub-objects, for merging determinations.Further, in another example, the compactness merger 132 may be disposedbefore the connectivity merger 130; and/or both components may analyzeimage data from each other to determine merging.

A method may be devised for separating a compound object intosub-objects in an image generated by an imaging apparatus. In oneembodiment, the method may be used by a threat determination system in asecurity checkpoint that screens passenger luggage for potential threatitems. In this embodiment, an ability of a threat determination systemto detect potential threats may be reduced if compound objects areintroduced, as computed properties of the compound object may not bespecific to a single physical object. Therefore, one may wish toseparate the compound object into distinct sub-objects of which it iscomprised.

FIG. 7 is a flow chart diagram of an example method 700 for separating acompound object into sub-objects in an image generated by an imagingapparatus. The example method 700 begins at 702 and involves identifyinga potential compound object based on an object's features, at 704. As anexample, one can identify candidate objects, from representations ofobjects in images from the imaging apparatus, for compound objectseparation. In one embodiment, available features of an object (e.g.,object properties, such as the object's shape properties) can becompared with pre-determined properties for known compound objects. Inthis way, for example, one can determine if the object in questioncomprises known compound object properties.

FIG. 12 is a flow chart diagram of an example embodiment 1200 ofidentifying potential compound objects 704. In this embodiment 1200, oneor more object features are compared to one or more corresponding objectfeature thresholds, at 1206. For example, a series of linear classifierscan be utilized, with respective classifiers corresponding to an objectfeature. The linear classifiers, in this example, can determine whetherthe object feature meets the threshold for a compound object, at 1208,thereby yielding a positive result for a compound object.

At 1210, in the example embodiment 1200, the one or more results of theobject feature comparisons can be combined, and at 1212, it can bedetermined whether the object is a potential compound object based onthe combined results. In this embodiment 1200, determining whether theobject is a potential compound object can be based on a desired numberof positive results from the feature comparison. For example, a desirednumber of positive results may be that all of the object features yielda positive result for a compound object. In this example, if any of thefeature comparisons yield a negative result, the object may be deemed tonot be a compound object, and, as such, can remain unchanged, at 1214.On the other hand, in this embodiment, if the object is determined to bea potential compound object, it may be sent for object splitting, at1216.

Turning back to FIG. 7, at 706 in the example method 700, a potentialcompound object can be split into sub-objects, which can compriseeroding one or more outer layers of volume space (e.g., voxels) from thepotential compound object. In one embodiment, weak connections in thepotential compound object can be broken by erosion of outer layers ofvolume space units. Further, in this embodiment, connectivity analysiscan be performed after respective erosions, and resulting sub-objectscan be labeled as separate objects.

FIG. 13 is a flow chart diagram of an example embodiment 1300 of thepotential compound object splitting (706). The example embodiment 1300begins at 1302 and involves identifying an outer layer of voxels on apotential compound object as a set of voxels to be eroded, at 1304. Forexample, a representation of a compound object in an image can comprisea group of connected voxels. In this example, an outer layer of thevoxels from the potential compound object in the image can be identifiedas a first set of voxels to be removed from the potential compoundobject.

At 1306, one or more outer layer of voxels can be eroded (removed) fromthe potential compound object. For example, the set of voxels identifiedin 1304 as the outer layer can be removed from the representation of thepotential compound object in the image. At 1308, a number of layersremoved can be identified. For example, one can count respective layersremoved by the erosion step in 1306.

At 1310, one can determine whether the potential compound object hasbeen separated into two or more sub-objects after respective erosions.For example, connectivity analysis can be used on the object in theimage to determine whether voxels are separated or remain spatiallyconnected. In this embodiment 1300, if it is determined that thepotential compound object has not been separated, one can return to step1304 and proceed through another layer erosion. However, if it isdetermined that the object has been separated, at 1312, respectiveidentified sub-object can be labeled as separate objects. For example,attaching labels to the sub-objects represented in the image by groupsof voxels can enable respective sub-objects to be identified asseparate, distinct objects in the image.

At 1314, in the example embodiment 1300, a number of sub-objects areidentified (e.g., counted). A sub-object count, for example, canfacilitate later volume reconstruction and possible merger ofsub-objects. Having counted a number of sub-objects, the exampleembodiment 1300 ends at 1316.

Turning back to FIG. 7, at 708, in the example method 700, image datavolume (e.g., for the sub-objects) is reconstructed, which can comprisegenerating sub-objects that have a combined volume approximate to thatof the potential compound object. For example, the erosion of one ormore layers of voxels from a representation of the potential compoundobject in the image can reduce a total volume of the resultingsub-objects, as volume space has been removed from the object. In orderto maintain a volume of the sub-objects close to the potential compoundobject, in this example, the volume of the sub-objects can bereconstructed by adding voxels back onto the sub-objects.

In one embodiment, reconstructing a volume of image data can comprisedissolving a set of eroded voxels into the sub-objects, where the set oferoded voxels comprises a layer of voxels that are spatially neighboringthe sub-objects' outer layer of voxels. For example, when voxels wereeroded from the outer layer of the object, the eroded voxels wereneighboring voxels left in the sub-objects. In this embodiment, theeroded voxels that neighbor voxels in the sub-objects can be returned tothe appropriate sub-object. Further, in this embodiment, if more thanone layer (set) of voxels was removed during erosion, for example, thelayers can be returned to the sub-object sequentially. In this way, inthis example, respective sub-objects can increase their volume (e.g., byadding voxels removed from them during erosion), while remainingdistinct and separate representations of objects in the image.

In another embodiment, reconstructing a volume of image data cancomprise dissolving a set of dilated voxels into the sub-objects, wherethe set of dilated voxels comprises voxels identified in an object by anobject dilator prior to identifying a potential compound object. In thisembodiment, for example, compound object separation may be a part of alarger object identification and threat determination method. In thisexample, during object identification, a dilator can be used. Dilationcan involve adding spatially connected voxels to an object, for example,in order to capture a desired amount of the representation of the objectin the image for identification. In this embodiment, those dilatedvoxels can be added to the sub-objects if the dilated voxels arespatially connected to the sub-objects. In this way, for example, avolume for the sub-objects can be increased, while maintainingsub-object separation.

Having reconstructed the volume of the sub-objects, the example method700 ends at 710.

In another aspect, some object obtained after volume reconstruction canbe a part of a same physical object. For example, the sub-objects mayactually belong connected together as one object, and not separated asdistinct objects. In one embodiment, different components that make up acompound object are typically weakly connected to one another, whilethose parts that are undesirably separated from a same physical objectare not weakly connected. In this embodiment, one can merge sub-objectsthat have greater connectivity to one another, while leaving separatedthose with less connectivity. In one example, connectivity can berepresented as an interface surface to object volume ratio, and theconnectivity can be compared to a threshold for compound objects.

FIG. 8 is a flow chart diagram 800 illustrating an example embodiment ofmerging connected sub-objects 802. At 804, one can determine aconnectivity ratio of connected sub-objects (e.g., a pair ofsub-objects). Determining a connectivity ratio, for example, can involvedividing a number of pairs of spatially connected voxels between theconnected sub-objects by a combined volume of the connected sub-objects,at 806. For example, for a pair of connected sub-objects, one candetermine a number of pairs of voxels that are connected between thesub-objects (e.g., each pair representing one voxel from respectivesub-objects on respective sides of the connection). In this example,this number can be divided by a total number of voxels from a union ofthe sub-objects, to yield a connectivity ratio for the sub-objects.

At 808, in the example embodiment 800, the connectivity ratio for thesub-objects is compared with a pre-determined connectivity threshold,for example, to determine whether the two objects should be merged orremain separated sub-objects. For example, if the connectivity ratio ofthe sub-objects is less than a threshold, which may be pre-determined bytesting known compound objects, the sub-objects are likely part of acompound object and remain separated.

FIG. 9 is diagram illustrating examples of connected sub-objects, andresulting merging based on connectivity. In these examples, sub-objects902 and 904 are connected at 906 by a number of pairs of connectedvoxels from respective sub-objects. In this example, after determiningwhether the connected sub-objects can be merged 908, the sub-objectsremain separated at 902 and 904, as the connectivity ratio between thetwo sub-objects may have been less than the threshold for mergingsub-objects.

Sub-objects 910 and 912 are connected at 914 by a greater number ofpairs of connected than 906. In this example, after determining whetherthe connected sub-objects can be merged 908, the sub-objects are mergedinto a single object at 916. In this example, the connectivity ratiobetween the two sub-objects 910 and 912 may have been greater than thethreshold for merging sub-objects.

In another embodiment, in this aspect, after connectivity merging somenon-compound objects may still be undesirably separated into distinctsub-objects. For example, while components of a same object can beexpected to form a spatially compact combination, parts of a compoundobject can be expected to form less compact combinations due to randompositions and orientations of various sub-parts of the compound object.In one embodiment, one can examine a pair of connected sub-objects anddetermine a difference between an Eigen-box fill ratio (EBFR) forrespective sub-objects and an EBFR for a union of the pair ofsub-objects (e.g., a potential compound object that is made up of twosub-objects). In this embodiment, for example, this difference may beused to determine if the sub-object can be merged into a single object.

FIG. 10 is a flow chart diagram 1000 illustrating an example embodimentof merging compact sub-objects 1002. At 1004, an Eigen-box is generatedfor respective sub-objects. For example, a box having a least volumethat can fit a sub-object can be generated for respective sub-objects ina pair of connected sub-objects. At 1006, an Eigen-box can be generatedfor a union of the sub-objects. For example, a box having a least volumethat can fit the union of the pair of sub-objects can be generated.

At 1008, in the example embodiment 1000, a fill ratio of an object in anEigen-box can be determined for the respective Eigen-boxes. For example,an EBFR can be determined for respective boxes containing thesub-objects in a pair of connected sub-objects, and an EBFR can bedetermined for the Eigen-box containing the union of the pair ofsub-objects. In one embodiment, determining a fill ratio for an objectcan comprise comparing a volume of the Eigen-box that is filled by theobject to a volume of the Eigen-box that is not filled by the object.For example, the comparison can yield a ratio of filled to unfilled, ora percentage of filled.

At 1010, a compactness ratio for the sub-objects can be determined,which can comprise comparing a combination of the fill ratios of thesub-object Eigen-boxes with the fill ratio of the sub-object unionEigen-box. For example, a fill ratio of a sub-object may be expected tobe greater than a fill ratio of a union of a pair of connectedsub-objects. In this example, a difference in the two fill ratios may bedetermined, and expressed as a compactness ratio for the sub-objects.

At 1012, the compactness ratio is compared to a pre-determinedcompactness threshold, for example, to determine whether the sub-objectscan be merged. For example, if the compactness ratio of the sub-objectsis greater than a threshold, which may be pre-determined by testingknown compound objects, the sub-objects are likely parts of a singlephysical object and can be merged.

FIG. 11 is a diagram illustrating examples of connected sub-objects, andresulting merging based on compactness. In the examples, an Eigen-box1106 has been generated for a union of a pair of connected sub-objects1102 and 1104. In this example, after comparing a compactness ratio forthe sub-objects to a compactness threshold, at 1108, the sub-objectsremain separated as distinct and separate objects. In this example, thecompactness ratio of the pair of connected sub-objects 1102 and 1104 mayhave been less than the compactness threshold.

In the examples of FIG. 11, an Eigen-box 1114 has been generated for aunion of a pair of connected sub-objects 1110 and 1112. In this example,after comparing a compactness ratio for the sub-objects to a compactnessthreshold, at 1108, the sub-objects are merged into a single object1116. In this example, the compactness ratio of the pair of connectedsub-objects 1110 and 1112 may have been greater than the compactnessthreshold.

It will be appreciated that, a potential compound object can beseparated into more than two sub-objects by the techniques describedabove. In one embodiment, for example, if more than two sub-objects areseparated from a potential compound object, more than two sub-objectsmay be merged. In this example, merging can comprise comparingconnectivity and/or compactness between a first pair of connectedsub-objects, then comparing connectivity and/or compactness between oneof the first pair of sub-objects and another connected sub-object thatwas not one of the first pair. Further, if a pair of connectedsub-objects is merged, merging can comprise comparing connectivityand/or compactness between the merged object to another sub-object thatis connected to the merged object.

Still another embodiment involves a computer-readable medium comprisingprocessor-executable instructions configured to implement one or more ofthe techniques presented herein. An example computer-readable mediumthat may be devised in these ways is illustrated in FIG. 14, wherein theimplementation 1400 comprises a computer-readable medium 1402 (e.g., aCD-R, DVD-R, or a platter of a hard disk drive), on which is encodedcomputer-readable data 1404. This computer-readable data 1404 in turncomprises a set of computer instructions 1406 configured to operateaccording to one or more of the principles set forth herein. In one suchembodiment 1400, the processor-executable instructions 1406 may beconfigured to perform a method, such as the example method 700 of FIG.7, for example. Many such computer-readable media may be devised bythose of ordinary skill in the art that are configured to operate inaccordance with one or more of the techniques presented herein.

Moreover, the words “example” and/or “exemplary” are used herein to meanserving as an example, instance, or illustration. Any aspect, design,etc. described herein as “example” and/or “exemplary” is not necessarilyto be construed as advantageous over other aspects, designs, etc.Rather, use of these terms is intended to present concepts in a concretefashion. As used in this application, the term “or” is intended to meanan inclusive “or” rather than an exclusive “or”. That is, unlessspecified otherwise, or clear from context, “X employs A or B” isintended to mean any of the natural inclusive permutations. That is, ifX employs A; X employs B; or X employs both A and B, then “X employs Aor B” is satisfied under any of the foregoing instances. In addition,the articles “a” and “an” as used in this application and the appendedclaims may generally be construed to mean “one or more” unless specifiedotherwise or clear from context to be directed to a singular form.

Also, although the disclosure has been shown and described with respectto one or more implementations, equivalent alterations and modificationswill occur to others skilled in the art based upon a reading andunderstanding of this specification and the annexed drawings. Thedisclosure includes all such modifications and alterations and islimited only by the scope of the following claims. In particular regardto the various functions performed by the above described components(e.g., elements, resources, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure which performs thefunction in the herein illustrated example implementations of thedisclosure. In addition, while a particular feature of the disclosuremay have been disclosed with respect to only one of severalimplementations, such feature may be combined with one or more otherfeatures of the other implementations as may be desired and advantageousfor any given or particular application. Furthermore, to the extent thatthe terms “includes”, “having”, “has”, “with”, or variants thereof areused in either the detailed description or the claims, such terms areintended to be inclusive in a manner similar to the term “comprising.”

What is claimed is:
 1. A system for compound object separation,comprising: a compound object splitter component configured to generate,from a representation of a potential compound object, a firstrepresentation of a first potential sub-object and a secondrepresentation of a second potential sub-object by eroding a first setof voxels from the representation of the potential compound object; anda volume reconstructor component configured to generate a first imagerepresenting the first potential sub-object from the firstrepresentation and a second image representing the second potentialsub-object from the second representation, the volume reconstructorcomponent comprising: a neighboring eroded voxel identifier componentconfigured to group one or more voxels, of the first set of voxels, thatare spatially connected to the first representation to generate a secondset of voxels; and an eroded voxel dissolver component configured todissolve the second set of voxels to generate at least a first portionof the first image.
 2. The system of claim 1, comprising a connectivitymerger component configured to merge the first image with the secondimage when a connectivity ratio for the first image and the second imageis within a pre-determined connectivity threshold.
 3. The system ofclaim 2, the connectivity ratio substantially corresponding to a numberof pairs of spatially connected voxels between the first image and thesecond image divided by a combined volume of the first potentialsub-object as represented by the first image and the second potentialsub-object as represented by the second image.
 4. The system of claim 1,comprising a compactness merger component configured to merge the firstimage with the second image when a compactness ratio for the first imageand the second image is within a pre-determined compactness threshold.5. The system of claim 4, the compactness merger component comprising:an Eigen-box generator component configured to generate a firstsub-object Eigen-box based upon the first image, a second sub-objectEigen-box based upon the second image, and a union Eigen-box for a unionof the first potential sub-object and the second potential sub-objectbased upon the first image and the second image; a fill ratio determinercomponent configured to calculate a first fill ratio for the firstsub-object Eigen box, a second fill ratio for the second sub-objectEigen box, and a third fill ratio for the union Eigen-box; and acompactness ratio generator component configured to compare the firstfill ratio, the second fill ratio, and the third fill ratio.
 6. Thesystem of claim 1, comprising an entry control component configured toidentify the representation of the potential compound object based uponone or more features of the potential compound object.
 7. The system ofclaim 1, the compound object splitter component comprising a volumespace eroder component configured to erode the first set of voxels fromthe representation of the potential compound object to generate aneroded representation of the potential compound object.
 8. The system ofclaim 7, comprising a sub-object identifier component configured toidentify the first representation and the second representation from theeroded representation of the potential compound object.
 9. The system ofclaim 1, the volume reconstructor component comprising a dilated voxeldissolver component configured to dissolve a third set of voxels togenerate at least a second portion of the first image.
 10. The system ofclaim 9, the volume reconstructor component comprising: a neighboringdilated voxel identifier component configured to identify one or morevoxels that have been dilated and that are spatially connected to thefirst representation to generate the third set of voxels.
 11. A methodfor compound object separation, comprising: eroding a first set ofvoxels from a representation of a potential compound object to generatea first representation of a first potential sub-object and a secondrepresentation of a second potential sub-object; generating a firstimage representing the first potential sub-object from the firstrepresentation; generating a second image representing the secondpotential sub-object from the second representation; generating a firstsub-object Eigen-box based upon the first image; generating a secondsub-object Eigen-box based upon the second image; generating a unionEigen-box for a union of the first potential sub-object and the secondpotential sub-object based upon the first image and the second image;determining a first fill ratio for the first sub-object Eigen-box, asecond fill ratio for the second sub-object Eigen box, and a third fillratio for the union Eigen-box; determining a compactness ratiodescribing a compactness between the first potential sub-object and thesecond potential sub-object based upon a comparison of the first fillratio, the second fill ratio, and the third fill ratio; and merging thefirst image with the second image when the compactness ratio meets apre-determined compactness threshold.
 12. The method of claim 11, themerging comprising merging the first image with the second image when aconnectivity ratio for the first image and the second image meets apre-determined connectivity threshold.
 13. The method of claim 12, theconnectivity ratio substantially corresponding to a number of pairs ofspatially connected voxels between the first image and the second imagedivided by a combined volume of the first potential sub-object asrepresented by the first image and the second potential sub-object asrepresented by the second image.
 14. The method of claim 11, thedetermining a first fill ratio comprising comparing a first volume ofthe first sub-object Eigen-box that is filled by the first potentialsub-object to a second volume of the first sub-object Eigen-box that isnot filled by the first potential sub-object.
 15. The method of claim11, comprising, before the eroding, identifying the representation ofthe potential compound object, the identifying comprising comparing oneor more object feature values to one or more corresponding objectfeature threshold values.
 16. The method of claim 11, the generating afirst image comprising at least one of: dissolving an eroded voxel; ordissolving a dilated voxel.
 17. A system for compound object separation,comprising: a compound object splitter component configured to generate,from a representation of a potential compound object, a firstrepresentation of a first potential sub-object and a secondrepresentation of a second potential sub-object by eroding a first setof voxels from the representation of the potential compound object; anda volume reconstructor component configured to generate a first imagerepresenting the first potential sub-object from the firstrepresentation and a second image representing the second potentialsub-object from the second representation, the volume reconstructorcomponent comprising: a neighboring dilated voxel identifier componentconfigured to identify one or more voxels that have been dilated andthat are spatially connected to the first representation to generate asecond set of voxels; and a dilated voxel dissolver component configuredto dissolve the second set of voxels to generate at least a firstportion of the first image.
 18. The system of claim 17, the volumereconstructor component comprising an eroded voxel dissolver componentconfigured to dissolve at least one voxel of the first set to generateat least a second portion of the first image.
 19. The system of claim17, comprising a compactness merger component configured to merge thefirst image with the second image when a compactness ratio for the firstimage and the second image is within a pre-determined compactnessthreshold.
 20. The system of claim 19, the compactness merger componentcomprising: an Eigen-box generator component configured to generate afirst sub-object Eigen-box based upon the first image, a secondsub-object Eigen-box based upon the second image, and a union Eigen-boxfor a union of the first potential sub-object and the second potentialsub-object based upon the first image and the second image; a fill ratiodeterminer component configured to calculate a first fill ratio for thefirst sub-object Eigen box, a second fill ratio for the secondsub-object Eigen box, and a third fill ratio for the union Eigen-box;and a compactness ratio generator component configured to compare thefirst fill ratio, the second fill ratio, and the third fill ratio.